Considering the exponential growth of today’s industry and the wastewater results of its processes, it needs to have an optimal treatment system for such effluent waters to mitigate the environmental impact generated by its discharges and comply with the environmental regulatory standards that are progressively increasing their demand. This leads to the need to innovate in the control and management information systems of the systems responsible to treat these residual waters in search of improvement. This paper proposes the development of an intelligent system that uses the data from the process and makes a prediction of its behavior to provide support in decision making related to the operation of the wastewater treatment plant (WWTP). To carry out the development of this system, a multilayer perceptron neural network with 2 hidden layers and 22 neurons each is implemented, together with process variable analysis, time-series decomposition, correlation and autocorrelation techniques; it is possible to predict the chemical oxygen demand (COD) at the input of the bioreactor with a one-day window and a mean absolute percentage error (MAPE) of 10.8%, which places this work between the adequate ranges proposed in the literature.
Among the most interesting areas of food technology research today is the search for natural alternatives to common sweeteners. One of these is Stevia rebaudiana Bertoni, which contains antioxidants and stevioside whose concentration can be affected by factors such as plant nutrition during its production. The objective of the study was to determine the effect of mineral nutrition levels (N, P, K Ca, Mg, S, Fe, Mn, B, Zn, Cu) in Steiner nutrient solution (SNS) on phenology and to quantify chlorophyll concentration (CC), stevioside content (SC), total phenolic compounds (TPC) and total flavonoids (TF) in the leaves of Stevia rebaudiana Bertoni. Five concentrations of SNS were evaluated: 0%, 25%, 50%, 75% and 100%. The experimental design was randomized with three replications. The experimental unit consisted of six plants per treatment (6 x 3 = 18 plants). The highest plant height (18.58 cm) was obtained with the 25% and 100% SNS concentrations; while the highest fresh weight and dry weight (21.70 ± 0.70 and 4.17 ± 0.12 g/ plant) were obtained with a 50% solution. The highest CC (41.31 ± 0.93 SPAD Units) and SC (8.88 ± 0.54 mg of stevioside / g dry matter (DM) were observed with 0% SNS. The highest contents of TF (16.42 ± 1.12 µg RE / g DM) and TPC (8.57 ± 1.12 µg GAE / g DM) were obtained with a 50% SNS solution. Finally, the compounds of interest were analyzed with respect to the total biomass obtained. The 75% SNS solution resulted in the highest amount of stevioside (17.03 mg / plant) and the second highest value for TPC (27.19 µg GAE / plant) and TF (64.57 µg RE / plant) content.
An important issue today for industries is optimizing their processes. Therefore, it is necessary to make the right decisions to carry out these activities, such as increasing the profit of businesses, improving the commercial strategies, and analyzing the industrial processes performance to produce better goods and services. This work proposes an intelligent system approach to prescribe actions and reduce the chemical oxygen demand (COD) in an equalizer tank of a wastewater treatment plant (WWTP) using machine learning models and genetic algorithms. There are three main objectives of this data-driven decision-making proposal. The first is to characterize and adapt a proper prediction model for the decision-making scheme. The second is to develop a prescriptive intelligent system based on expert’s rules and the selected prediction model’s outcomes. The last is to evaluate the system performance. As a novelty, this research proposes the use of long short-term memory (LSTM) artificial neural networks (ANN) with genetic algorithms (GA) for optimization in the WWTP area.
A más de doscientos años de la promulgación de la Constitución Política de los Estados Unidos Mexicanos, garante del derecho del trabajo, es preciso analizar la manera en cómo ha incidido en la realidad de los trabajadores. El objetivo del artículo consistió en conocer las condiciones laborales de trabajadores jalpenses para analizar la manera en que éstas inciden en su desarrollo personal y familiar. Para identificar diferencias entre las condiciones de trabajadores formales e informales se optó por un estudio de caso. Entrevistas en profundidad facilitaron particularizar cada situación. El derecho positivo laboral ha incidido favorablemente en los trabajadores formales llevándolos a expresar satisfacción con su actividad, al asegurar su bienestar personal y de sus familias. No fue el caso de los trabajadores informales para quienes la jornada de trabajo, salario, aguinaldo y demás prestaciones incumplían lo pactado en la ley; repercutiendo en su satisfacción, expectativas laborales y relaciones familiares. Se concluye que no basta con la normativización positiva de las condiciones ideales en las que debe presentarse un trabajo digno para transformar las condiciones mínimas de desarrollo de los trabajadores.
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